NoisET: Noise learning and Expansion detection of T-cell receptors

TL;DR

NoisET is a Python package that models and accounts for biological and experimental noise in T-cell receptor sequencing data, enabling more accurate detection of immune responses across various datasets.

Contribution

The paper introduces NoisET, a user-friendly Python tool that generalizes Bayesian noise modeling for T-cell receptor repertoire analysis, improving the detection of responding clones.

Findings

Effective noise modeling across multiple sequencing technologies

Successful identification of responding clonotypes in vaccination data

Open-source availability of the NoisET package

Abstract

High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and in healthy individuals. However quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. We review methods for accounting for both biological and experimental noise and present an easy-to-use python package NoisET that implements and generalizes a previously developed Bayesian method. It can be used to learn experimental noise models for repertoire sequencing from replicates, and to detect responding clones following a stimulus. We test the package on different repertoire sequencing technologies and datasets. We review how such approaches have been used to identify responding clonotypes in vaccination and disease data. Availability: NoisET is freely available to use with source code at github.com/statbiophys/NoisET.